Weaviate

Description

Weaviate is a cloud-native, real-time vector search engine (aka neural search engine or deep search engine). There are modules for specific use cases such as semantic search, plugins to integrate Weaviate in any application of your choice, and a console to visualize your data.

GraphQL - RESTful - vector search engine - vector database - neural search engine - semantic search - HNSW - deep search - machine learning - kNN

Documentation

You can find detailed documentation in the developers section of our website or directly go to one of the docs using the links in the list below.

• Documentation
  • About Weaviate
    • Introduction video
    • What is Weaviate
    • Why a vector search engine?
    • How does Weaviate work?
    • When should I use Weaviate?
  • Installation
    • Docker Compose
    • Cloud Deployment
    • Weaviate Cluster Service (beta)
    • Kubernetes
  • Modules
  • RESTful API references
  • GraphQL references
  • Client Libraries
    • Python
    • JavaScript
    • Go
  • How To's
  • RESTful API references
  • GraphQL references
  • More resources

Examples

You can find more examples here

Unmask Superheroes in 5 steps using the NLP module

A simple example in Python (you can also use other client libs) showing how Weaviate can help you unmask superheroes thanks to its vector search capabilities 🦸

1. Connect to a Weaviate

import weaviate
client = weaviate.Client("http://localhost:8080")

2. Add a class to the schema

classObj = {
    "class": "Superhero",
    "description": "A class describing a super hero",
    "properties": [
        {
            "name": "name",
            "dataType": [
                "string"
            ],
            "description": "Name of the super hero"
        }
    ],
    "vectorizer": "text2vec-contextionary" # Tell Weaviate to vectorize the content
}
client.schema.create_class(classObj) # returns None if successful

Step 3. Add the superheroes with a batch request

batman = {
    "name": "Batman"
}
superman = {
    "name": "Superman"
}
batch = weaviate.ObjectsBatchRequest()
batch.add(batman, "Superhero")
batch.add(superman, "Superhero")
client.batch.create(batch)

Step 4. Try to find superheroes in the vectorspace

def findAlterego(alterEgo):
    whoIsIt = client.query.get(
        "Superhero",
        ["name", "_additional {certainty, id } "]
    ).with_near_text({
        "concepts": [alterEgo] # query that gets vectorized 🪄
    }).do()

    print(
        alterEgo, "is", whoIsIt['data']['Get']['Superhero'][0]['name'],
        "with a certainy of", whoIsIt['data']['Get']['Superhero'][0]['_additional']['certainty']
    )

findAlterego("Clark Kent")  # prints something like: Clark Kent is Superman with a certainy of 0.6026741
findAlterego("Bruce Wayne") # prints something like: Bruce Wayne is Batman with a certainy of 0.6895526

Step 5. See how the superheroes are represented in the vectorspace

def showVectorForAlterego(alterEgo):
    whoIsIt = client.query.get(
        "Superhero",
        ["_additional {id} "]
    ).with_near_text({
        "concepts": [alterEgo] # query that gets vectorized 🪄
    }).do()

    getVector = client.data_object.get_by_id(
        whoIsIt['data']['Get']['Superhero'][0]['_additional']['id'],
        additional_properties=["vector"]
    )

    print(
        "The vector for",
        alterEgo,
        "is",
        getVector['vector']
    )

showVectorForAlterego("Clark Kent") # prints something like: The vector for Clark Kent is [-0.05484624, 0.08283167, -0.3002325, ...etc...

Support

• Stackoverflow for questions
• Github for issues
• Slack channel

Contributing

• How to Contribute